Neural networks : the official journal of the International Neural Network Society
Feb 9, 2021
Phenomenon of stochastic separability was revealed and used in machine learning to correct errors of Artificial Intelligence (AI) systems and analyze AI instabilities. In high-dimensional datasets under broad assumptions each point can be separated f...
Computer methods and programs in biomedicine
Feb 7, 2021
BACKGROUND: Mathematical modeling of vector-borne diseases and forecasting of epidemics outbreak are global challenges and big point of concern worldwide. The outbreaks depend on different social and demographic factors based on human mobility struct...
Neural networks : the official journal of the International Neural Network Society
Jan 30, 2021
Studies have demonstrated that stochastic configuration networks (SCNs) have good potential for rapid data modeling because of their sufficient adequate learning power, which is theoretically guaranteed. Empirical studies have verified that the learn...
Synchronization is a collective mechanism by which oscillatory networks achieve their functions. Factors driving synchronization include the network's topological and dynamical properties. However, how these factors drive the emergence of synchroniza...
Neural networks : the official journal of the International Neural Network Society
Dec 3, 2020
This paper investigates the state bounding problem of fuzzy memristive neural networks (FMNNs) with bounded input disturbances. By using the characters of Metzler, Hurwitz and nonnegative matrices, this paper obtains the exact delay-independent and d...
We discuss stability analysis for uncertain stochastic neural networks (SNNs) with time delay in this letter. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and utilizing Wirtinger inequalities for estimating the integral inequalitie...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
Recently, the dynamical behaviors of coupled neural networks (CNNs) with and without reaction-diffusion terms have been widely researched due to their successful applications in different fields. This article introduces some important and interesting...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
In the literature, the effects of switching with average dwell time (ADT), Markovian switching, and intermittent coupling on stability and synchronization of dynamic systems have been extensively investigated. However, all of them are considered sepa...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
In this article, the finite-time H state estimation problem is addressed for a class of discrete-time neural networks with semi-Markovian jump parameters and time-varying delays. The focus is mainly on the design of a state estimator such that the co...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
Deep neural networks (DNNs) are widely used and demonstrated their power in many applications, such as computer vision and pattern recognition. However, the training of these networks can be time consuming. Such a problem could be alleviated by using...
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